Diagnostic device for use with automatic control systems

ABSTRACT

In an automatic control system, controls receive an input indicative of a deviation between feedback and demand signals and produce an output which in turn is applied to a plant to control it. A diagnostic device includes a model having an equivalent characteristic to the controls and receives the deviation signal. When a noise coming into the automatic control system is detected or when a deviation between the outputs of the model and controls is sufficiently small, no model operation is performed, and the output of the controls is determined as that of the model. However, when the deviation between the outputs of the model and the controls is large, the model is supplied with an input thereby performing a model operation. A resultant output of the model is then compared with the output of the controls to produce an alarm indicative of an abnormal state of the controls in accordance with a deviation between these outputs.

BACKGROUND OF THE INVENTION

This invention relates to a device for detecting an abnormal state ofcontrols or of a plant, which is an object to be controlled by theformer, in an automatic control system. More particularly, the inventionrelates to a diagnostic device for use with automatic control systemswhich is based on a model reference method wherein there is provided amodel having a characteristic simulated to that of the controls orplant, and outputs of the model and plant are compared for detecting anabnormal state of the plant or the like.

A widespread use has been made of automatic control systems inindustrial fields, and many of them are of a so-called closed loop typeutilizing a feedback signal. More particularly, a plant, which is anobject to be controlled, is operated by the output signal from controlswhile the controls are operated depending on the deviation between theplant output and the demand signal corresponding thereto. The output ofplant is generally termed the feedback signal. In such automatic controlsystems, in the event of failure of the control system, there occurs acontrolling out of the command of the demand signal originally intendedand a consequent overrunning or running-down of the plant. In view oftrouble shooting, the running-down of the plant has a tendency towardoverall safety in general, and usually invites no serious problems.However, it can be disagreeable in a case where the plant controllableby the control system in question is connected with additional plantsbecause the additional systems of plant may be adversely affected. Thelarger and more sophisticated the systems are, the more seriously theytend to be effected. Conversely, overrunning is often fatal, since itresults in the possibility of serious damage of the component apparatus.

For the above reasons, in the past, it was the practice to quicklydetect failures of the control system through various methods and takecare of the plant suitably. One of the conventional methods fordetection of the abnormal state of a control system is to monitor thedeviation signal between the demand and feedback signals, as disclosed,for example, in Japanese Patent Publication No. 6815/72. This method isbased on the fact that "the demand signal substantially coincides withthe feedback signal under the stationary condition." Although having thecapability of detecting the failure with high accuracy under thestationary condition, this method has many difficulties with thedetection under the transient condition. For example, when the demandvalue is changed, the absolute value of the deviation signal increases.But this fact alone makes no distinction between a change of demandvalue (normal) and a failure of the system (abnormal). Additionally,with a retard response of the plant, the state remaining in increaseddeviation continues for a long time. Therefore, an increase in detectionsensitivity of the diagnostic device is prone to an erroneousdiscrimination; conversely, for elimination of the erroneousdiscrimination, the detection sensitivity must be decreased. In anyevent, the deviation monitor method fails to detect the abnormal stateunder the transient condition.

Recently, in place of the aforementioned method, a method called themodel reference method has been highlighted, as exemplified in aJapanese patent application laid open to the public as No. 58279/73,wherein there is provided a model having an input-output characteristic##EQU1## for example, where G₁ (S) represents an input-outputcharacteristic of the controls and G₂ (S) represents an input-outputcharacteristic of the plant. The model is supplied with a deviationsignal between the demand and feedback signals so that the output of themodel is compared with the feedback signal representative of the outputfrom the plant. The above W(S) is generally termed the total transferfunction. In the model reference method, the model W(S) may beformulated in an analog or digital expression, and, especially, it isdesirable to digitally formulate a sophisticated W(S) by means of amicrocomputer or similar device. If there occurs no failure in thefeedback control system whose abnormal state is to be detected, thesignal passed through the model W(S) coincides with the feedback signalirrespective of any change in the demand signal, and the deviationsignal between these two signals is always zero. Thus, in contrast tothe aforementioned deviation monitor method in which the deviationsignal varies under the transient condition even when the system is in anormal state, this model reference method is freed from variations inthe deviation signal when the system is in the normal state so thatdetection sensitivity can be increased. At the same time, the erroneousdiscrimination is suppressed from occurring. Actually, however, thismethod, when reduced to practice, suffers from variations in thedeviation signal for various causes even when no failure of an object tobe diagnosed occurs, thereby giving rise to erroneous discrimination.Typical causes of this are:

I. Interference by noise

An object to be diagnosed standing for a plant or an analog automaticcontrol system generally has a sophisticated characteristic expression.Consequently, the model is usually programmed for a digital operationdevice such as a computer instead of being constituted by an analogoperation circuit. Thus, in order for an operation by the modelprogrammed in a computer to be executed, it is necessary to convertanalog outputs at component apparatus in the automatic control systeminto digital quantities. In other words, the analog output is sampled ata predetermined period Δt for its conversion into a digital signal of nbits. Digital signals, however, are sensitive to noises and the noisesinterfere with any bits of the digital signal at the same probability.No serious problems are raised if the noise interferes with bits of alower digit, but the noise interfering with bits of an upper digitaffects the model operation to a great extent, resulting in erroneousdiscrimination. Further, the actuation of various switches in theautomatic control system causes direct spike noises to come into theanalog signal, and it happens that the sampling is carried out insynchronism with the spike noise.

II. Accuracy of the model

Practically, it is difficult to completely simulate a model to acharacteristic of an object to be diagnosed. In particular, even if themodel is formulated in a mathematical expression, there still remaindifficulties with letting parameters in the mathematical expressioncoincide with an actually existing object to be diagnosed. Adjustment ofsuch parameters will require characteristic tests on the object in manycases. However, complete characteristic tests are theoreticallyimpossible, and therefore these tests should be terminated with areasonable degree of incompletion. Since errors due to the parameters ofthe model are accumulated when the model has integrating terms, thedeviation between outputs of the object and the model is increased witha lapse of time and erroneous detections of abnormal states result.

SUMMARY OF THE INVENTION

An object of this invention is to provide a diagnostic device based on amodel reference method in which even with a model whose parameters willnot completely coincide with those of an object to be detected,detection errors due to errors in a model operation can be eliminated.

Another object of this invention is to provide a diagnostic device basedon the model reference method capable of preventing erroneous alarmseven when interferences by spike noises occur.

According to the invention, a diagnostic device comprises a model havingan equivalent input-output characteristic to that of an object to bediagnosed, which model receives a model input which is an input to theobject. By comparing a model output with an output from the object, themodel output is determined as the object output when a resultantdeviation is small. On the other hand, the model is supplied with theinput signal to perform a model operation only when the deviation islarge. Thereafter, a comparison is made between the model operationoutput and the object output to produce an alarm in accordance with adeviation resulting from this comparison.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic diagram of a diagnostic device embodying theinvention especially designed for detecting abnormal states of controls.

FIG. 2 is a schematic device embodying the invention especially fordetecting abnormal states of a plant.

FIG. 3 is a schematic diagram of a diagnostic device embodying theinvention especially designed for combination with a feed-water systemin a heat power station.

FIGS. 4a and 4b are flow chart diagrams prepared for performing thedetection of abnormal states of controls by means of a digital computerin accordance with the present diagnostic device.

FIG. 5 is a graphical representation for explaining the operation ofspike noise detection.

FIGS. 6a to 6c are graphical representations for explaining, withreference to FIGS. 4a and 4b, operations in a normal state, withinterfering spike noise, and in an abnormal state of controls,respectively.

FIGS. 7a and 7b are flow chart diagrams prepared for performing thedetection of abnormal states of the plant by means of the digitalcomputer in accordance with the present diagnostic device.

FIG. 8 is a graphical representation showing a characteristic offunction generator means when the gain G(L(t)) in step 8' of FIG. 7 isvariable.

DESCRIPTION OF THE PREFERRED EMBODIMENTS

Referring now to the drawing, FIG. 1 shows a basic circuit arrangementsuitable for applying a diagnostic device B of the present invention toa closed loop control system A which is especially designed formonitoring abnormal states of controls alone, where G₁ (S) represents atransfer function of the controls. The diagnostic device B receives acontrol demand signal x₁ and calculates a deviation E₂ between the modeloutput signal x_(3*) and a feedback signal x₃ of this closed loop systemthrough a detecting object model G₁ '(S) so as to discriminate abnormalstates depending on the value of the deviation E₂ and produce an alarmsignal F when these abnormal states occur.

In addition to the construction performing the operation describedabove, this invention additionally comprises a model correcting unit 1.This model correcting unit 1 receives as inputs the demand signal x₁,deviation signal E₂ and feedback x₃, and produces a model operationresult correcting signal c through a model operation error detectingcircuit 2 and a model operation correcting circuit 3, thereby correctingthe output signal of the controls G₁ (S).

FIG. 2 shows one embodiment suitable for detecting abnormal states of aplant G₂ (S), and differs from FIG. 1 in that a model G₂ '(S) handlesinput and output signals, both being different from those of FIG. 1.

The function of the model correcting units in FIGS. 1 and 2 will bedescribed later specifically by referring to flow charts.

FIG. 3 shows a block diagram for clarifying the input-output relation,in which a diagnostic device of the present invention is applied to afeed-water system for a boiler in a heat power plant. A generaldescription will now be given of the heat power plant wherein afeed-water regulator valve 6 stands for an object to be controlled.Water vapor generated in a boiler is supplied through a turbineregulator valve 20 to a generator turbine 21 thereby to drive it. Agenerator 22 converts the mechanical power of the turbine into theelectrical power. Water vapor sent from the turbine 21 is fed to acondenser 9 and cooled therein by cooling water to turn into water.Water from the condenser is again supplied to the boiler through afeed-water pump 8. In such a feed-water system that, the water sent fromthe condenser 9 is so pressurized in the feed-water pump 8 as to be fedto the boiler, and the feed-water pump 8 is driven by a feed-water pumpdrive turbine 7 which is operated, for example, with bleeding vapor fromthe generator turbine 21. Feed-water controls A effects aproportional-integrating control of the regulator valve 6 provided forthe generator turbine so as to let a feed-water quantity x₂ from thefeed-water pump 8 coincide with a demand signal x₁. A diagnostic deviceB is adapted to provide an early detection of failures of the feed-watercontrols A and may be realized as shown in FIG. 2 by receiving as inputsfeed-water command signal x₁, feed-water quantity signal x₂ andfeed-water control signal x₃, and delivering as an output the alarmsignal F. In comparing of FIG. 2 with FIG. 3, it will be seen that G₁(S) corresponds to a characteristic of the proportional-integratingcontrols 10 and that G₂ (S) corresponds to a characteristic of acomponent involving valve 6, turbine 7 and pump 8.

FIGS. 4a and 4b show an operation flow when the diagnostic device B ofFIG. 3 is simulated by a digital computer. Step S1, an input section,receives the feed-water command signal, feed-water quantity signal, andfeed-water control signal and formulates them as X₁ (t), X₂ (t) and X₃(t), respectively.

Hereinafter, a description will be given of a diagnosis of the controls,followed by a description of a diagnosis of the plant.

The operation flow will be described in the order of operation steps byreferring to functions of blocks shown in FIG. 1 for betterunderstanding of the invention.

The model G₁ '(S) of FIG. 1 will first be discussed which performsoperations as shown in steps S8 and S9 in FIG. 4b.

From equation (1), a deviation E₁ (t) between a demand signal X₁ (t) anda feedback signal X₂ (t) is obtained:

    E.sub.1 (t)=X.sub.1 (t)-X.sub.2 (t)                        (1)

A model output X₃ *(t) is then obtained from equation (2): ##EQU2##where X₃ * and E₁ suffixed with (t) represent values of X₃ * and E₁which are sampled and fetched at time t. Similarly, X₃ * and E₁ suffixedwith (t-Δt) represent values of X₃ * and E₁ sampled and fetched at atime in advance of t by Δt. This meaning of the suffix is also valid forexplaining the time relationship between digital signals.

The above equation (2) digitally expresses a model derived from transferfunction G₁ (S) of the analog controls shown in FIG. 1. That is to say,G₁ '(S) can be expressed by the following equation (4) in terms of aLaplace operator S which, in turn, may be replaced by equation (5) interms of time-domain, bearing in mind the fact that 1/S means theintegration in time-domain: ##EQU3## where G in equation (4) representsa gain and T represents an integral time constant. By differentiatingrighthand and lefthand terms is equation (5), equation (6) is obtainedwhich in turn may be replaced by equation (7) in the form of a finitedifference formula, bearing in mind the fact that differential term dX₃/dt, for example, is a calculation to obtain a rate of change of X₃within Δt: ##EQU4## where Δt in equation (7) has the meaning of asampling period in the case of an input of digital signals, and (t-Δt)corresponds to a time in advance of t by one sampling period.

From equation (7), X₃ (t) may be expressed by equation (8) which is acharacteristic equation of the digital model: ##EQU5## As the modeloutput, X₃ (t) may be expressed by using X₃ *(t) as shown in FIG. 2 sothat equation (8) may be replaced by equation (9): ##EQU6##

This model equation (9) is valid for the proportional-integratingoperation. If the controls effect aproportional-integrating-differentiating control, a model equation suchas expressed by equation (10) may be obtained through a similaranalysis: ##EQU7## where Kp represents a differential coefficient.

Thus, the model is formulated by the above equations and operatedthrough steps S8 and S9 in FIG. 4b.

Next, the function of the model operation error detecting circuit 2 willbe explained. This function includes two partial functions, i.e., aspike detecting function and a discrimination function which determinesthe necessity of the model operation by monitoring the deviation E₂between controls output X₃ and model output X₃ *.

I. Spike detecting function

This function is carried out through steps S2, S3 and S4 in FIG. 4a. Instep S2, a rate of change DX₁ (t) of a command signal X₁ is calculatedin accordance with equation (11):

    DX.sub.1 (t)=X.sub.1 (t)-X.sub.1 (t-Δt)              (11),

where, as mentioned in the foregoing description, X₁ (t) represents avalue of X₁ which is sampled and fetched at time t and X₁ (t-Δt)represents a value of X₁ sampled and fetched at a time in advance of tby one sampling period Δt. This rule is also applicable to X₂ and X₃.

In steps S3 and S4, the presence or absence of spike noise contained inthe command signal X₁ (t) will be determined in accordance with thefollowing equations (12) and (13) by using DX₁ obtainable from equation(11):

    {DX.sub.1 (t-Δt)≧L.sub.2 } {-DX.sub.1 (t)≧L.sub.2 }(12),

    {-DX.sub.1 (t-Δt)≧L.sub.2 } {DX.sub.1 (t)≧L.sub.2 }(13),

where the symbol " " represents the logic "AND."

The spike noise can be detected in accordance with equations (12) and(13), as will be detailed by referring to FIG. 5. In equation (12), itis meant by the fact that righthand and lefthand terms are satisfiedsimultaneously that a sharp spike noise occurs in the positive-goingdirection. In equation (13), it is meant by the same fact as in equation(12) that a sharp spike noise occurs in the negative-going direction. Asshown in FIG. 5, the feed-water command signal X₁ remains substantiallyunchanged until time t(4), from which it increases gradually until timet(9). If a spike noise interferes with the command signal at time t(6),X₁ (6)>X₁ (7)>X₁ (5) stands, where X₁ (5), X₁ (6) and X₁ (7) are valuesof X₁ at times t(5), t(6) and t(7), respectively. Between times t(6) andt(5), the difference DX₁ (6) occurs as expressed in equation (12)':

    DX.sub.1 (6)=X.sub.1 (6)-X.sub.1 (5)>0                     (12)',

and the difference DX₁ (7) occurs between times t(7) and t(6) asexpressed in equation (12)":

    DX.sub.1 (7)=X.sub.1 (7)-X.sub.1 (6)<0                     (12)".

Equations (12)' and (12)" correspond to the lefthand and righthand termsof equation (12), respectively, so that if DX₁ (6) and -DX₁ (7) arerespectively larger than L₂, the positive spike noise can be detected.For convenience of explanation, X₁ (t) is illustrated in FIG. 5 ashaving a sufficiently large gradient. Actually, however, a change of X₁(t) within Δt is sufficiently small as compared with the noise. Thisensures that the noise can be detected at high sensitivity. Obviously,L₂ is made larger than a normal change of X₁ (t), i.e., ΔX₁ (t) withinΔt. As gathered from equation (12), the detection of the positive spikenoise is effected by catching such a change of DX₁ as sharply increasingat one instant and sharply decreasing at a subsequent instant.Similarly, in accordance with equation (13), the negative spike noisecan be detected by catching such a change of DX₁ as sharply decreasingat one instant and sharply increasing at a subsequent instant.

The spike noise interfering with X₁ can be detected in this manner, forexample. Similarly, spike noises interfering with X₂ and X₃ can bedetected. In the flow chart of FIGS. 4a and 4b, only the spike noisedetection for X₁ is formulated without referring to that for X₂ and X₃.For the spike noise detection for X₂ and X₃, a similar program may beperformed, provided that X₁ and DX₁ in steps S2, S3 and S4 are replacedby X₂ and DX₂, and X₃ and DX₃, respectively. It should be understoodthat accordance to this spike noise detecting method, the generation ofa spike is detected at a sampling period which is next to a samplingperiod at which the spike occurs.

If no spike noise is detected in steps S3 and S4, step S5 follows,whereas with the spike noise detected, step S6 follows. In other words,when the spike noise occurs, the model operation is prevented; and onlywhen no spike noise occurs, the model operation is permitted.

II. Discrimination function for determining necessity of the modeloperation

As described in the foregoing description, it is almost impossible toprovide the model with parameters which completely coincide with thecharacteristic of controls. Accordingly, the model expression ofequation (2) will inevitably contain errors. Thus, attention should bedrawn to the fact that the second and third terms in equation (2) usedata obtained from the preceding sampling so that executing theoperation of equation (2) at every sampling time only accumulateserrors. Therefore, the present invention prevents the model operationwhen the object assumes the normal state and permits the model operationaccording to equation (2) only when the object seems to assume anyabnormal states. During the normal state, the model output X₃ *(t) ismade coincident with the output X₃ (t) of controls. That is to say, theinitial value is set to X₃ (t). This ensures that errors in the modelwill not be accumulated, whereby the model operation and the diagnosiscan be performed with high accuracy during abnormal states. Conversely,without strict adjustment of the model parameter, a highly accuratemodel comparison method can be ensured.

Firstly, in step S5, deviation E₂ (t) between X₃ (t) and X₃ *(t) iscalculated, followed by calculating the rate of change DE₂ (t) of thedeviation E₂ (t):

    E.sub.2 (t)=X.sub.3 (t)-X.sub.3 *(t) (14),

    DE.sub.2 (t)=E.sub.2 (t)-E.sub.2 (t-Δt) (15)

If DE₂ (t) is less than a threshold L₃ and the following equation (16)stands, the device determines that the object is normal, followed byadvancing to step S13. However, if equation (16) does not stand, thedevice, advancing to step S8, executes the model operation so as tocheck whether or not the controls are abnormal:

    {DE.sub.2 (t)<L.sub.3 }                                    (16)

In this manner, the necessity of model operation is discriminated. Butvarious alternatives may be thought of:

(1) In the aforementioned embodiment, in order to derive the variationin deviation E₂ (t), the rate of change is calculated over the periodbetween (t-Δt) and t, but it is otherwise expedient to statisticallydeal with rates of change measured within a long time interval, wherebyinfluence of the stationary noise is eliminated. Thus, for example, therate of change DE₂ (t) may be computed in accordance with equation (17):

    DE.sub.2 (t)=k.sub.1 DE.sub.2 (t-Δt)+k.sub.2 DE.sub.2 (t) (17),

where k₁ and k₂ represents constants and k₁ +k₂ =1.

(2) By varying the threshold L₃ used in step S7 in accordance withequation (18), it is possible to prevent inactivation of the diagnosticdevice even when such a non-directional failure as hunting occurs:

    L.sub.3 =k{X.sub.3 *(t)-X.sub.3 *(t-Δt)}             (18),

where k is constant.

Next, the alarm function will be described. The absolute value of E₂ (t)is first computed from equation (19) and then compared with L₁ todiscriminate whether or not an alarm should be provided:

    E.sub.2 (t)={X.sub.3 (t)-X.sub.3 (t)}                     (19),

    E.sub.2 (t)=L.sub.1                                        (20).

Briefly, when the absolute value of the deviation between model outputX₃ *(t) and controls output X₃ (t) is larger than L₁, an alarmindicative of an abnormal state of the controls is provided, oncondition that two or more successive abnormal states are detected. Thisfunction corresponds to steps S10, S11, S14 and S15 in FIG. 4.

In the case of steps S11 and S14 delivering "NO" or following steps S15and S13, elapse of the sampling period Δt is checked in step S16, andthereafter the step returns to S1 to repeat a similar program.

From the above explanation of all of the functions, it will beappreciated that the correction of model operation as described withreference to FIG. 1 corresponds to coincidence of initial values in stepS13.

Under the normal operation of the controls, the aforementioned programnormally proceeds in an orderly manner, tracing the following steps. Inthe absence of the spike noise, steps S3 and S4 determine "NO" by theirdiscriminations so that a series of steps S1→S2→S3→S4→S5→S6 is effected.If step S7 judges that the controls are still normal, |DE₂ (t)| issufficiently less than L₃ and, accordingly, the step S7 determines"YES," whereby one sampling period execution is completed, tracing stepsS7→S13→S16. To sum this up, as far as the controls are normal and nointerference of the spike noise occurs, the model performs onlycoincidence of the initial values (step S13). This is graphicallyillustrated in FIG. 6a wherein since the model output X₃ *, dottedlines, is nearly equal to the controls output X₃, solid lines, and |DE₂(t)| is less than L₃, the model output X₃ * is rendered identical withthe controls output X₃ every sampling time. This coincidence operationfor initial values is effected every sampling time under the normaloperation.

Reference is now made to FIG. 6b for discussing the presence of thespike noise. In this case, it is assumed that the spike noise interfereswith the controls output at time t₅ and the coincidence of initialvalues described with reference to FIG. 6a has been completed in advanceof time t₄. Although the noise actually occurs at t₅, it cannot bedetected according to steps S3 and S4 because at this time only therising or falling of the noise is observed. It is not until time t₆, atwhich subsequent falling or rising of the noise is observed, that thespike noise can be detected. Accordingly, steps S3 and S4 determine"NO," followed by step S7. In this step S7, if the spike noise having asufficiently large magnitude is interfering with the controls output X₃,|DE₂ (t)| becomes larger than L₃ and step S7 determines "NO." As aresult, X₃ *(t) will be operated in step S9. In this case, however, thesecond term GE₁ (t-Δt) is zero because this term corresponds to GE₁which has been observed at the preceding sampling time at which GE₁ hasnot been computed. The initial execution of the operation according tostep S9 always has its second term nullified. When two or moreexecutions are to be effected, the second term associated with thesecond (inclusive) or more execution can have somewhat perceptiblevalues. This is equivalent to the fact that the initial value ofintegration upon execution of the proportional-integrating expression instep S9 is zero. Subsequently, in step S10, X₃ * is compared with X₃ toproduce a sufficiently large deviation since the X₃ contains the spikenoise, so that step S11 determines "YES." Because of the firstdetermination, however, step S15 will not provide any alarm. Theoperation associated with time t₅ has now been completed. At t₆, thenoise contained is detected through step S3 or S4, determining "YES."Consequently, similarly to the normal operation as illustrated in FIG.6a, the prosecution is completed following the coincidence of initialvalues alone. Since steps S14 and S15 are not executed, thediscrimination as represented by E₂ (t)≧L₁ in step S14 is omitted, andno alarm being provided. If no interference of noise occurs after t₇, asimilar operation to FIG. 6a will be performed.

Additionally, if no interference of noise with X₃ occurs at t₅ withwhich step S7 is now associated, step S7 determines "YES" so that nooperation is performed after step S8 quite similarly to the normal stateof FIG. 6a.

Finally, reference is made to FIG. 6c for discussing the abnormal stateof the controls. It is assumed that the abnormal state occurs at timet₅, and, accordingly, the controls output X₃ begins to increase steeply.As already described with reference to FIG. 6a, only the coincidence ofinitial values was completed before time t₄. Since X₃ increases rapidlyat t₅, |DE₂ (t)| becomes larger than L₃ in step S7, thereby determining"NO." The model output X₃ * is operated in step S9 and compared with X₃in step S10. Because of the rapid increase in X₃ and consequent E₂ (t)being larger than L₁, step S10 determines "YES." For the prosecution att₅, the "YES" determination in step S11 is the first or initial one,and, is therefore followed by steps through steps S14 and S16 toterminate the prosecution. At time t₆, the prosecution is effectedthrough steps S1→S2→S3→S4→S5→S6→S7→S8.fwdarw.S9→S10→S11, wherein E₂ (t)also becomes larger than L₁ and step S11 determines "YES." As a result,two successive determinations of "YES" by step S11 take place, so thatan alarm for the abnormal state of controls being provided.

As will be understood from the above description, according to theinvention, two or more determinations of "YES" by step S11 lead to theprovision of the alarm. Further, since only the coincidence of initialvalues is normally executed, errors in the model cannot be accumulated.The model operation is effected only when the deviation between X₃ * andX₃ is large ensure a highly accurate model for the diagnosis.

Now, apart from the controls, a further embodiment will be describedwherein diagnosis based on the model comparison method is applied to aplant. This diagnosis, as shown in FIG. 2, differs from FIG. 1 in that amodel G₂ '(S) receives a feedback signal X₂ alone. FIGS. 7a and 7b showa flow chart for explaining the diagnosis for a plant, which flow chartis almost similar to that of FIGS. 4a and 4b. The model of FIG. 2 isdifferent from that of FIG. 1 in its model characteristic in the firstplace.

Actually, the model of the plant is much more sophisticated than that ofthe controls but it is not always necessary for the intended diagnosisto accurately express the former model. Therefore, according to theinvention, the plant model is expressed in terms of the linear delay inwhich gain G is a function of load L: ##EQU8## Bearing in mind the factthat a Laplace operator S means the differential, equation (21) may beexpressed in the time-domain by equation (22): ##EQU9## Equation (22)may be replaced by equation (23) by converting dX₂ (t)/dt into a finitedifference expression: ##EQU10## where X₂ *(t) represents the modeloutput.

Thus, X₂ *(t) is expressed by equation (25), through ##EQU11##

Equation (25) indicates the model, where although being variable withthe magnitude of load L(t), the G(L(t)) may be calculated by making itvariable with the output X₃ (t) of the controls since the load L(t)corresponds to the controls output X₃ (t), for example.

This model operation is effected by steps S8' and S9' in FIG. 7b,particularly, the step S8' putting G(L(t))=G(X₃ (t)) for varying gainG(L(t)) with X₃ (t). This gain may be predetermined as a function ofFIG. 8, for example. Step S9' executes equation (25).

In addition to the above difference, the diagnosis for a plant has stepsS5', S6', S7' and S13' which are different from the corresponding stepsof FIGS. 4a and 4b, and, for distinction, are assigned a symbol of dash(').

For steps S5', S6', S7', S13', S10' and S11', since the plant diagnosismonitors the feedback signal X₂ (t) in contrast to the controls outputX₃ (t) in FIGS. 4a and 4b, corresponding equations are changed asfollows:

    E.sub.3 (t)=X.sub.2 (t)-X.sub.2 *(t) (for S5'),

    DE.sub.3 (t)=E.sub.3 (t)-E.sub.3 (t-Δt)              (for S6'),

    |DE.sub.3 (t)|<L.sub.4                   (for S7'),

    X.sub.2 *(t)=X.sub.2 (t)                                   (for S13'),

    E.sub.3 (t)=|X.sub.3 *(t)-X.sub.3 (t)|   (for S10'),

    E.sub.3 (t)≧L.sub.5                                 (for S11').

As compared with FIGS. 4a and 4b, FIGS. 7a and 7b handle the differentcharacteristic. Further, FIGS. 7a and 7b compare the plant output X₂ (t)with the model output X₂ (t) to differ from FIGS. 4a and 4b comparingthe controls output X₃ (t) with the model output X₃ *(t). All but thesedifferences, the two of the diagnosis for the controls and the plant arebased on the same essential idea, and the operation according to theflow chart of FIGS. 7a and 7b will therefore not be further detailed.Needless to say, steps S2, S3 and S4 in FIG. 7a may be applicable to X₂and X₃ as in these steps in FIG. 4a.

Flow charts of FIGS. 4a, 4b and FIGS. 7a, 7b, although having beenillustrated independently hereinbefore, may be combined, in which caseit is expedient to clearly discriminate whether abnormal states occur inthe controls or in the plant.

It should be understood from the foregoing description that since,according to the invention, only the coincidence of initial values isnormally effected, the execution can be shortened. This ensures thatsaved time within the sampling time pays to different purposes such thatthe computer can be operated at high efficiency. Normally, the modeloperation of step S9 is not executed to prevent accumulation ofoperation errors and, hence, the model operation per se can be effectedwith high accuracy. The occurrence of the spike noise also prevents themodel operation, thereby preventing an erroneous alarm from beingprovided.

We claim:
 1. A diagnostic device for use with an object to be diagnosed,said object having an automatic control system forming a closed loop andincluding an integration element in said object, said diagnostic devicesampling the input and output of said object with a fixed periodic timeso as to obtain digital signals, and detecting an abnormal state of saidobject in accordance with said digital signals so as to indicate theoccurrence of the abnormal state, said diagnostic devicecomprising:mathematical model means simulating an input-outputcharacteristic of said object to be diagnosed; first means for applyingto said model means said sampled input of said object in the presentperiod of time when said object output satisfies a predeterminedcondition for letting the model means execute a calculation to obtain anoutput of said model means corresponding to the output of said object,and for letting the model means output coincide with the object outputin the present period of time without executing the model meanscalculation when said object output does not satisfy said predeterminedcondition, said predetermined condition being that a rate of change ofsaid object output is larger than a predetermined value, so thataccumulation of errors in the model means calculation when thepredetermined condition is not satisfied is avoided; and second meansfor comparing the output of said model means controlled by said firstmeans with the output of said object in the present period of time, andthen for detecting an abnormal state of said object in accordance with adeviation output obtained by the comparison so as to indicate theoccurrence of the abnormal state.
 2. A diagnostic device according toclaim 1, wherein said first means lets the model means output coincidewith the object output in the present sampling period of time withoutexecuting the model means calculation when one of the input and outputof said object is sharply changed at one instant and sharply changed inthe opposite direction at a subsequent instant.
 3. A diagnostic deviceaccording to claim 1, wherein said second means indicates the occurrenceof the abnormal state when said second means detects several consecutiveoccurrences of the abnormal states.